Pandas1 submitted

[PranathiDeepak]

No description provided.

[super30admin]

1. Problem1.py:

   • Correctness: The code correctly creates a DataFrame from a list of lists with specified column names. This is a basic but correct implementation.
   • Time Complexity: O(n) where n is the number of elements in the list, which is optimal for this operation.
   • Space Complexity: O(n) for storing the DataFrame, which is necessary.
   • Code Quality: The code is simple and readable. However, it could benefit from a docstring explaining its purpose.
   • Efficiency: No optimizations needed for this simple task.

2. Problem2.py:

   • Correctness: The solution correctly filters countries based on area and population criteria. The return statement properly selects the required columns.
   • Time Complexity: O(n) for filtering the DataFrame, which is optimal.
   • Space Complexity: O(n) in the worst case if all countries meet the criteria.
   • Code Quality: The code is clean and follows good practices. The type hints are a nice touch.
   • Efficiency: The solution is already efficient. No improvements needed.

3. Problem3.py:

   • Correctness: The solution correctly filters products that are both low fat and recyclable, returning only the product_id as required.
   • Time Complexity: O(n) for filtering, which is optimal.
   • Space Complexity: O(n) in the worst case if all products meet the criteria.
   • Code Quality: The code is concise and readable. Type hints are properly used.
   • Efficiency: The solution is efficient. No improvements needed.

4. Problem4.py:

   • Correctness: The solution correctly identifies customers who have not placed any orders using a left merge and filtering for NaN values in customerId. The final output is formatted correctly.
   • Time Complexity: O(n + m) for the merge operation where n and m are the sizes of the DataFrames, which is standard for this operation.
   • Space Complexity: O(n + m) for the merged DataFrame, which is necessary.
   • Code Quality: The code is well-structured and readable. The renaming of the column in the return statement is a good practice.
   • Efficiency: The solution is efficient. No improvements needed.

General Feedback:

• The student demonstrates a good understanding of pandas operations and DataFrame manipulations.
• All solutions are correct and efficient for their respective problems.
• The code quality is high, with proper use of type hints and clean syntax.
• Adding docstrings to explain the purpose of each function would improve readability and maintainability.
• The student could consider adding comments for complex operations to aid understanding, though the current solutions are straightforward enough that comments may not be strictly necessary.